低信噪比下短波授时信号的去噪方法研究
|
摘要
提出一种基于经验模态分解与改进型谱减法相结合的低信噪比短波时号语音增强方法,解决在复杂噪声环境下短波时号无法用于定时的问题.该方法的核心思想是利用希尔伯特-黄变换(HHT)算法对带噪短波时号进行经验模态分解,通过最大相关度筛选出含有短波时号的固有模态分量进行重构,再对重构之后的信号进行谱减,从而达到降噪的目的.实验表明:该方法的降噪效果优于传统方法.
A speech enhancement algorithm based on the Empirical mode decomposition(EMD) and improved spectral subtraction is proposed for the low SNR(Signal Noise Ratio) short-wave time signal. This method is proposed to solve the problem that the shor-twave time signals cannot be used for timing in complex noise environments.The core idea of this method is to use the Hilbert-Huang Transform(HHT) algorithm to decompose the noised short-wave signals with the empirical mode decomposition, and to select the intrinsic mode functions containing the shor-twave signals reconstruction through the maximum correlation. Then the reconstructed signal is spectrally subtracted to achieve the purpose of noise reduction. Experiments show that this method has a better noise reduction than the traditional methods.
A speech enhancement algorithm based on the Empirical mode decomposition(EMD) and improved spectral subtraction is proposed for the low SNR(Signal Noise Ratio) short-wave time signal. This method is proposed to solve the problem that the shor-twave time signals cannot be used for timing in complex noise environments.The core idea of this method is to use the Hilbert-Huang Transform(HHT) algorithm to decompose the noised short-wave signals with the empirical mode decomposition, and to select the intrinsic mode functions containing the shor-twave signals reconstruction through the maximum correlation. Then the reconstructed signal is spectrally subtracted to achieve the purpose of noise reduction. Experiments show that this method has a better noise reduction than the traditional methods.
引文
[1]蒙智谋.时间频率学报,2014,37:145
[2]Huang N E,Zhen S,Steven R L.et al.RSPSA,1998,454:903
[3]Huang N E,Chen X Y,Lo M T,et al.Advances in Adaptive Data Analysis,2011,3:63
[4]Zou X J,Li X Y,Lo M T,et al.International Multi-Symposiums on Computer and Computational Sciences,2006,1:208
[5]漆贯荣.时间科学基础.北京:高等教育出版社,2006:75
[2]Huang N E,Zhen S,Steven R L.et al.RSPSA,1998,454:903
[3]Huang N E,Chen X Y,Lo M T,et al.Advances in Adaptive Data Analysis,2011,3:63
[4]Zou X J,Li X Y,Lo M T,et al.International Multi-Symposiums on Computer and Computational Sciences,2006,1:208
[5]漆贯荣.时间科学基础.北京:高等教育出版社,2006:75